SoroushVahidi/scidocs huggingface dataset:
Achievement Goals in the Classroom : Students ' Learning Strategies and Motivation Processes
(2001)

We studied how specific motivational processes are related to the salience of mastery and performance goals in actual classroom settings. One hundred seventy-six students attending a junior high/high school for academically advanced students were randomly selected from one of their classes and responded to a questionnaire on their perceptions of the classroom goal orientation, use of effective learning strategies, task choices, attitudes, and causal attributions. Students who perceived an emphasis on mastery goals in the classroom reported using more effective strategies, preferred challenging tasks, had a more positive attitude toward the class, and had a stronger belief that success follows from one's effort. Students who perceived performance goals as salient tended to focus on their ability, evaluating their ability negatively and attributing failure to lack of ability. The pattern and strength of the findings suggest that the classroom goal orientation may facilitate the maintenance of adaptive motivation patterns when mastery goals are salient and are adopted by students.
Calibrating the COCOMO II Post-Architecture Model
(1997)

The COCOMO II model was created to meet the need for a cost model that accounted for future software development practices. This resulted in the formulation of three submodels for cost estimation, one for composing applications, one for early lifecycle estimation and one for detailed estimation when the architecture of the product is understood. This paper describes the calibration procedures for the last model, Post-Architecture COCOMO II model, from eighty-three observations. The results of the multiple regression analysis and their implications are discussed. Future work includes further analysis of the PostArchitecture model, calibration of the other models, derivation of maintenance parameters, and refining the effort distribution for the model output.
Meditation states and traits: EEG, ERP, and neuroimaging studies.
(2006)

Neuroelectric and imaging studies of meditation are reviewed. Electroencephalographic measures indicate an overall slowing subsequent to meditation, with theta and alpha activation related to proficiency of practice. Sensory evoked potential assessment of concentrative meditation yields amplitude and latency changes for some components and practices. Cognitive event-related potential evaluation of meditation implies that practice changes attentional allocation. Neuroimaging studies indicate increased regional cerebral blood flow measures during meditation. Taken together, meditation appears to reflect changes in anterior cingulate cortex and dorsolateral prefrontal areas. Neurophysiological meditative state and trait effects are variable but are beginning to demonstrate consistent outcomes for research and clinical applications. Psychological and clinical effects of meditation are summarized, integrated, and discussed with respect to neuroimaging data.
Weighted Association Rule Mining using weighted support and significance framework
(2003)

We address the issues of discovering significant binary relationships in transaction datasets in a weighted setting. Traditional model of association rule mining is adapted to handle weighted association rule mining problems where each item is allowed to have a weight. The goal is to steer the mining focus to those significant relationships involving items with significant weights rather than being flooded in the combinatornal explosion of insignificant relationships. We identify the challenge of using weights in the iterative process of generating large itemsets. The problem of invalidation of the "downward closure property" in the weighted setting is solved by using an improved model of weighted support measurements and exploiting a "weighted downward closure property". A new algorithm called WARM (Weighted Association Rule Mining) is developed based on the improved model. The algorithm is both scalable and efficient in discovering significant relationships in weighted settings as illustrated by experiments performed on simulated datasets.
A low-power Adaboost-based object detection processor using Haar-like features
(2013)

This paper presents an architecture of a low-power real-time object detection processor using Adaboost with Haar-Like features. We employ a register array based architecture, and introduce two architectural-level power optimization techniques; signal gating domain for integral image extraction, and low-power integral image update. The power efficiency of our proposed architecture including nine classifiers is estimated to be 0.64mW/fps when handling VGA(640 × 480) 70fps video.
A study on evolution of email spam over fifteen years
(2013)

Email spam is a persistent problem, especially today, with the increasing dedication and sophistication of spammers. Even popular social media sites such as Facebook, Twitter, and Google Plus are not exempt from email spam as they all interface with email systems. With an “arms-race” between spammers and spam filter developers, spam has been continually changing over the years. In this paper, we analyze email spam trends on a dataset collected by the Spam Archive, which contains 5.1 million spam emails spread over 15 years (1998-2013). We use statistical analysis techniques on different headers in email messages (e.g. content type and length) and embedded items in message body (e.g. URL links and HTML attachments). Also, we investigate topic drift by applying topic modeling on the content of email spam. Moreover, we extract sender-to-receiver IP routing networks from email spam and perform network analysis on it. Our results show the dynamic nature of email spam over one and a half decades and demonstrate that the email spam business is not dying but changing to be more capricious.
Applying persuasive design in a diabetes mellitus application
(2013)

This paper describes persuasive design methods and compares this to an application currently under development for diabetes mellitus patients. Various elements of persuasion and a categorization of persuasion types are mentioned. Also discussed are principles of how successful persuasion should be designed, as well as the practical applications and ethics of persuasive design. This paper is not striving for completeness of theories on the topic, but uses the theories to compare it to an application intended for diabetes mellitus patients. The results of this comparison can be used for improvements of the application.
Analysis of modulation schemes for Bluetooth-LE module for Internet-of-Things (IoT) applications
(2018)

Bluetooth transceivers have been in the active area of recent research being the key component of physical layer in the Bluetooth technology. The low power consumption of Bluetooth low energy (LE) devices compared to the conventional Bluetooth devices has enhanced its importance in Internet-of-Things (IoT) applications. Therefore, Bluetooth low energy device based solution needs expansion in the IoT network infrastructure. The transceivers in the literature and modulation schemes are compared and summarized. Energy consumption of modulation schemes in Bluetooth communication are analyzed and compared using the model presented in this work. In this approach considering both circuit and signal power consumption, optimum modulation order for minimum energy consumption has been found using numerical calculation and relation between signal to noise ratio (SNR) and channel capacity. Battery life for IoT sensors using Bluetooth LE technology as a wireless link has been analyzed considering multiple transaction times for transmitters having different power consumption. MFSK and more bandwidth-efficient GFSK are identified as low energy solution for all smart devices.
Wheat glutenin subunits and dough elasticity: findings of the EUROWHEAT project
(2000)

*IACR-Long Ashton Research Station, Department of Agricultural Sciences, University of Bristol, Long Ashton BS41 9AF, UK (tel: +1275-392181; fax: +1275-394299; e-mail: peter.shewry@bbsrc.ac.uk) {Institut National de la Recherche Agronomique, Centre de Recherches de Nantes, Laboratoire de BiochimieetdeTechnologiedesProtéines,B.P.71627, Ruede laGéraudière,Nantes 44316,Cedex03, France xDipartimento di Agrobiologia ed Agrochimica, Via San Camillo de Lellis, Viterbo 01100, Lazio, Italy {Institute of Food Research, Norwich Laboratory, Norwich Research Park, Colney Lane, Norwich NR4 7UA, UK
Insights into Human Behavior from Lesions to the Prefrontal Cortex
(2014)

The prefrontal cortex (PFC), a cortical region that was once thought to be functionally insignificant, is now known to play an essential role in the organization and control of goal-directed thought and behavior. Neuroimaging, neurophysiological, and modeling techniques have led to tremendous advances in our understanding of PFC functions over the last few decades. It should be noted, however, that neurological, neuropathological, and neuropsychological studies have contributed some of the most essential, historical, and often prescient conclusions regarding the functions of this region. Importantly, examination of patients with brain damage allows one to draw conclusions about whether a brain area is necessary for a particular function. Here, we provide a broad overview of PFC functions based on behavioral and neural changes resulting from damage to PFC in both human patients and nonhuman primates.
A lane detection algorithm based on reliable lane markings
(2018)

This paper proposes a robust and effective vision-based lane detection approach. First, two binary images are obtained from the region of interest of gray-scale images. The obtained binary images are merged by a novel neighborhood AND operator and then transformed to a bird's eye view (BEV) via inverse perspective mapping. Then, gaussian probability density functions are fit to the left and right regions of a histogram image acquired from the BEV. Finally, a polynomial lane model is estimated from the identified regions. Experimental results show that the proposed method accurately detects lanes in complex situations including worn-out and curved lanes.
Socio-cognitive gamification: general framework for educational games
(2015)

Gamification of learning material has received much interest from researchers in the past years. This paper aims to further improve such learning experience by applying socio-cognitive gamification to educational games. Dynamic difficulty adjustment (DDA) is a well-known tool in optimizing gaming experience. It is a process to control the parameters in a video game automatically based on user experience in real-time. This method can be extended by using a biofeedback-approach, where certain aspects of the player’s ability is estimated based on physiological measurement (e.g. eye tracking, ECG, EEG). Here, we outline the design of a biofeedback-based framework that supports dynamic difficulty adjustment in educational games. It has a universal architecture, so the concept can be employed to engage users in non-game contexts as well. The framework accepts input from the games, from the physiological sensors and from the so-called supervisor unit. This special unit empowers a new social aspect by enabling another user to observe or intervene during the interaction. To explain the game-user interaction itself in educational games we propose a hybrid model.
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles
(2017)

Sample complexity and safety are major challenges when learning policies with reinforcement learning for real-world tasks – especially when the policies are represented using rich function approximators like deep neural networks. Modelbased methods where the real-world target domain is approximated using a simulated source domain provide an avenue to tackle the above challenges by augmenting real data with simulated data. However, discrepancies between the simulated source domain and the target domain pose a challenge for simulated training. We introduce the EPOpt algorithm, which uses an ensemble of simulated source domains and a form of adversarial training to learn policies that are robust and generalize to a broad range of possible target domains, including to unmodeled effects. Further, the probability distribution over source domains in the ensemble can be adapted using data from target domain and approximate Bayesian methods, to progressively make it a better approximation. Thus, learning on a model ensemble, along with source domain adaptation, provides the benefit of both robustness and learning/adaptation.
DeepLoco: dynamic locomotion skills using hierarchical deep reinforcement learning
(2017)

Learning physics-based locomotion skills is a difficult problem, leading to solutions that typically exploit prior knowledge of various forms. In this paper we aim to learn a variety of environment-aware locomotion skills with a limited amount of prior knowledge. We adopt a two-level hierarchical control framework. First, low-level controllers are learned that operate at a fine timescale and which achieve robust walking gaits that satisfy stepping-target and style objectives. Second, high-level controllers are then learned which plan at the timescale of steps by invoking desired step targets for the low-level controller. The high-level controller makes decisions directly based on high-dimensional inputs, including terrain maps or other suitable representations of the surroundings. Both levels of the control policy are trained using deep reinforcement learning. Results are demonstrated on a simulated 3D biped. Low-level controllers are learned for a variety of motion styles and demonstrate robustness with respect to force-based disturbances, terrain variations, and style interpolation. High-level controllers are demonstrated that are capable of following trails through terrains, dribbling a soccer ball towards a target location, and navigating through static or dynamic obstacles.
Terrain-adaptive locomotion skills using deep reinforcement learning
(2016)

Reinforcement learning offers a promising methodology for developing skills for simulated characters, but typically requires working with sparse hand-crafted features. Building on recent progress in deep reinforcement learning (DeepRL), we introduce a mixture of actor-critic experts (MACE) approach that learns terrain-adaptive dynamic locomotion skills using high-dimensional state and terrain descriptions as input, and parameterized leaps or steps as output actions. MACE learns more quickly than a single actor-critic approach and results in actor-critic experts that exhibit specialization. Additional elements of our solution that contribute towards efficient learning include Boltzmann exploration and the use of initial actor biases to encourage specialization. Results are demonstrated for multiple planar characters and terrain classes.
The Arcade Learning Environment: An Evaluation Platform for General Agents
(2013)

In this article we introduce the Arcade Learning Environment (ALE): both a challenge problem and a platform and methodology for evaluating the development of general, domain-independent AI technology. ALE provides an interface to hundreds of Atari 2600 game environments, each one different, interesting, and designed to be a challenge for human players. ALE presents significant research challenges for reinforcement learning, model learning, model-based planning, imitation learning, transfer learning, and intrinsic motivation. Most importantly, it provides a rigorous testbed for evaluating and comparing approaches to these problems. We illustrate the promise of ALE by developing and benchmarking domain-independent agents designed using well-established AI techniques for both reinforcement learning and planning. In doing so, we also propose an evaluation methodology made possible by ALE, reporting empirical results on over 55 different games. All of the software, including the benchmark agents, is publicly available.
Software development governance: A meta-management perspective
(2009)

Software development governance is a nascent field of research. Establishing how it is framed early, can significantly affect the progress of contributions. This position paper considers the nature and role of governance in organizations and in the software development domain in particular. In contrast to the dominant functional and structural perspectives, an integrated view of governance is proposed as managing the management of a particular domain (that is, a meta-management perspective). Principles are developed and applied to software development governance and illustrated by a case study.
Automated extraction of product comparison matrices from informal product descriptions
(2017)

Domain analysts, product managers, or customers aim to capture the important features and differences among a set of related products. A case-by-case reviewing of each product description is a laborious and time-consuming task that fails to deliver a condense view of a family of product. In this article, we investigate the use of automated techniques for synthesizing a product comparison matrix (PCM) from a set of product descriptions written in natural language. We describe a tool-supported process, based on term recognition, information extraction, clustering, and similarities, capable of identifying and organizing features and values in a PCM – despite the informality and absence of structure in the textual descriptions of products. We evaluate our proposal against numerous categories of products mined from BestBuy. Our empirical results show that the synthesized PCMs exhibit numerous quantitative, comparable information that can potentially complement or even refine technical descriptions of products. The user study shows that our automatic approach is capable of extracting a significant portion of correct features and correct values. This approach has been implemented in MatrixMiner a web environment with an interactive support for automatically synthesizing PCMs from informal product descriptions. MatrixMiner also maintains traceability with the original descriptions and the technical specifications for further refinement or maintenance by users. Preprint submitted to JSS January 5, 2017
A General Lower Bound on the Number of Examples Needed for Learning
(1988)

We prove a lower bound of ( ln + VCdim(C) ) on the number of random examples required for distribution-free learning of a concept class C, where VCdim(C) is the Vapnik-Chervonenkis dimension and and are the accuracy and con dence parameters. This improves the previous best lower bound of ( ln + VCdim(C)), and comes close to the known general upper bound of O( ln + VCdim(C) ln ) for consistent algorithms. We show that for many interesting concept classes, including kCNF and kDNF, our bound is actually tight to within a constant factor. A. Ehrenfeucht was supported by NSF grant MCS-8305245, D. Haussler by ONR grant N00014-86-K-0454, M. Kearns by ONR grant N00014-86-K-0454 and an A.T. & T. Bell Laboratories Scholarship, and L. Valiant by grants ONR-N00014-85-K-0445, NSF-DCR-8600379, DAAL03-86-K-0171 and by the SERC of the U.K. Part of this research was done while M. Kearns was visiting U.C. Santa Cruz.
On-demand virtualization for live migration in bare metal cloud
(2017)

The level of demand for bare-metal cloud services has increased rapidly because such services are cost-effective for several types of workloads, and some cloud clients prefer a single-tenant environment due to the lower security vulnerability of such enviornments. However, as the bare-metal cloud does not utilize a virtualization layer, it cannot use live migration. Thus, there is a lack of manageability with the bare-metal cloud. Live migration support can improve the manageability of bare-metal cloud services significantly.
 This paper suggests an on-demand virtualization technique to improve the manageability of bare-metal cloud services. A thin virtualization layer is inserted into the bare-metal cloud when live migration is requested. After the completion of the live migration process, the thin virtualization layer is removed from the host. We modified BitVisor [19] to implement on-demand virtualization and live migration on the x86 architecture.
 The elapsed time of on-demand virtualization was negligible. It takes about 20 ms to insert the virtualization layer and 30 ms to remove the one. After removing the virtualization layer, the host machine works with bare-metal performance.
Gender identity and sexual orientation in women with borderline personality disorder.
(2011)

INTRODUCTION
In the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, text revision (DSM-IV-TR) (and earlier editions), a disturbance in "identity" is one of the defining features of borderline personality disorder (BPD). Gender identity, a person's sense of self as a male or a female, constitutes an important aspect of identity formation, but this construct has rarely been examined in patients with BPD.


AIMS
In the present study, the presence of gender identity disorder or confusion was examined in women diagnosed with BPD.


MAIN OUTCOME MEASURES
We used a validated dimensional measure of gender dysphoria. Recalled gender identity and gender role behavior from childhood was also assessed with a validated dimensional measure, and current sexual orientation was assessed by two self-report measures.


METHODS
A consecutive series of 100 clinic-referred women (mean age, 34 years) with BPD participated in the study. The women were diagnosed with BPD using the International Personality Disorder Exam-BPD Section.


RESULTS
None of the women with BPD met the criterion for caseness on the dimensional measure of gender dysphoria. Women who self-reported either a bisexual or a homosexual sexual orientation had a significantly higher score on the dimensional measure of gender dysphoria than the women who self-reported a heterosexual sexual orientation, and they also recalled significantly more cross-gender behavior during childhood. Results were compared with a previous study on a diagnostically heterogeneous group of women with other clinical problems.


CONCLUSION
The importance of psychosexual assessment in the clinical evaluation of patients with BPD is discussed.
Enhanced Classification Model for Cervical Cancer Dataset based on Cost Sensitive Classifier Hayder
(2017)

Cervical cancer threatens the lives of many women in our world today. In 2014, the number of women infected with this disease in the United States was 12,578, of which 4,115 died, with a death rate of nearly 32%. Cancer data, including cervical cancer datasets, represent a significant challenge data mining techniques because absence of different costs for error cases. The proposed model present a cost sensitive classifiers that has three main stages; the first stage is prepressing the original data to prepare it for classification model which is build based on decision tree classifier with cost selectivity and finallyevaluation the proposed model based on many metrics in addition to apply a cross validation.The proposed model provides more accurate result in both binary class and multi class classification. It has a TP rate (0.429) comparing with (0.160) for typical decision tree in binary class task.
Sequencing technologies — the next generation
(2010)

Demand has never been greater for revolutionary technologies that deliver fast, inexpensive and accurate genome information. This challenge has catalysed the development of next-generation sequencing (NGS) technologies. The inexpensive production of large volumes of sequence data is the primary advantage over conventional methods. Here, I present a technical review of template preparation, sequencing and imaging, genome alignment and assembly approaches, and recent advances in current and near-term commercially available NGS instruments. I also outline the broad range of applications for NGS technologies, in addition to providing guidelines for platform selection to address biological questions of interest.